import { Index } from "@upstash/vector"
import { personalInfo, workExperience, extracurricularExperience, skills, projects, education } from "../lib/resume-data"
import { readFileSync } from "fs"
import { resolve } from "path"
import OpenAI from "openai"
// Load environment variables from .env.local
try {
const envPath = resolve(process.cwd(), '.env.local')
const envContent = readFileSync(envPath, 'utf-8')
envContent.split('\n').forEach(line => {
const match = line.match(/^([^=:#]+)=(.*)$/)
if (match) {
const key = match[1].trim()
const value = match[2].trim().replace(/^["'](.*)["']$/, '$1')
process.env[key] = value
}
})
console.log("✅ Loaded environment variables from .env.local")
} catch (error) {
console.warn("⚠️ Could not load .env.local file")
}
// Initialize clients
function getVectorClient() {
const url = process.env.UPSTASH_VECTOR_REST_URL
const token = process.env.UPSTASH_VECTOR_REST_TOKEN
if (!url || !token) {
throw new Error("Missing Upstash Vector configuration")
}
return new Index({ url, token })
}
function getOpenAIClient() {
const apiKey = process.env.OPENAI_API_KEY
if (!apiKey) {
throw new Error("Missing OpenAI API key")
}
return new OpenAI({ apiKey })
}
// Generate embeddings using OpenAI
async function generateEmbedding(text: string): Promise<number[]> {
const openai = getOpenAIClient()
const response = await openai.embeddings.create({
model: "text-embedding-3-small",
input: text,
})
return response.data[0].embedding
}
async function updateVectorDatabase() {
console.log("🚀 Starting Upstash Vector Database Update with OpenAI Embeddings...")
try {
const index = getVectorClient()
console.log("✅ Connected to Upstash Vector Database")
// Create chunks from updated resume data
const textChunks: Array<{id: string, text: string, metadata: any}> = []
// Personal Info
textChunks.push({
id: "personal-info",
text: `${personalInfo.name} - ${personalInfo.title}: ${personalInfo.subtitle}. Located in ${personalInfo.location}. ${personalInfo.summary}`,
metadata: {
content: `${personalInfo.name} is a ${personalInfo.title} specializing in ${personalInfo.subtitle}. Located in ${personalInfo.location}. Contact: ${personalInfo.contact.email}, ${personalInfo.contact.phone}. ${personalInfo.summary}`,
source: "Personal Information",
category: "Overview"
}
})
// Work Experience
workExperience.forEach((job, i) => {
textChunks.push({
id: `work-${i}`,
text: `${job.position} at ${job.company} (${job.duration}, ${job.location}). ${job.description}`,
metadata: {
content: `${job.position} at ${job.company}, ${job.location} (${job.duration}). ${job.description} Achievements: ${job.achievements.join(". ")}`,
source: job.company,
category: "Work Experience"
}
})
})
// Extracurricular
extracurricularExperience.forEach((exp, i) => {
textChunks.push({
id: `extra-${i}`,
text: `${exp.role} at ${exp.organization} (${exp.duration}). ${exp.description}`,
metadata: {
content: `${exp.role} at ${exp.organization} (${exp.duration}). ${exp.description} Achievements: ${exp.achievements.join(". ")}`,
source: exp.organization,
category: "Extracurricular"
}
})
})
// Skills
const skillCategories = Object.entries(skills)
skillCategories.forEach(([category, skillList], i) => {
const skillNames = skillList.map(s => s.name).join(", ")
textChunks.push({
id: `skills-${i}`,
text: `${category}: ${skillNames}`,
metadata: {
content: `Technical skills in ${category}: ${skillNames}`,
source: "Skills",
category: "Skills"
}
})
})
// Projects
projects.forEach((proj, i) => {
textChunks.push({
id: `project-${i}`,
text: `${proj.name} - ${proj.description}`,
metadata: {
content: `Project: ${proj.name}. ${proj.description} Outcomes: ${proj.outcomes.join(". ")}`,
source: proj.name,
category: "Projects"
}
})
})
// Education
education.forEach((edu, i) => {
textChunks.push({
id: `edu-${i}`,
text: `${edu.degree} from ${edu.institution}, ${edu.location}. Graduated ${edu.graduation}`,
metadata: {
content: `Education: ${edu.degree} in ${edu.field} from ${edu.institution}, ${edu.location}. Graduated ${edu.graduation}.`,
source: edu.institution,
category: "Education"
}
})
})
console.log(`📦 Created ${textChunks.length} text chunks`)
console.log("🔄 Generating embeddings with OpenAI...")
// Generate embeddings and prepare vectors
const vectors: Array<{id: string, vector: number[], metadata: any}> = []
for (let i = 0; i < textChunks.length; i++) {
const chunk = textChunks[i]
console.log(` Embedding ${i + 1}/${textChunks.length}: ${chunk.id}`)
const embedding = await generateEmbedding(chunk.text)
vectors.push({
id: chunk.id,
vector: embedding,
metadata: chunk.metadata
})
// Small delay to avoid rate limits
await new Promise(resolve => setTimeout(resolve, 100))
}
console.log("✅ All embeddings generated")
// Upload in batches
const BATCH_SIZE = 10
for (let i = 0; i < vectors.length; i += BATCH_SIZE) {
const batch = vectors.slice(i, i + BATCH_SIZE)
console.log(`⬆️ Uploading batch ${Math.floor(i / BATCH_SIZE) + 1}/${Math.ceil(vectors.length / BATCH_SIZE)}...`)
await index.upsert(batch)
}
console.log("✅ Upload complete!")
console.log(`📊 Total vectors uploaded: ${vectors.length}`)
// Test query
console.log("\n🔍 Testing search...")
const testEmbedding = await generateEmbedding("Where does Binal live?")
const testResults = await index.query({
vector: testEmbedding,
topK: 3,
includeMetadata: true
})
console.log(`✅ Search test successful! Found ${testResults.length} results`)
if (testResults.length > 0 && testResults[0].metadata) {
console.log("Sample result:", (testResults[0].metadata.content as string)?.substring(0, 150) + "...")
}
console.log("\n✅ Database update completed successfully!")
} catch (error: any) {
console.error("❌ Error during update:", error)
throw error
}
}
// Run the update
updateVectorDatabase()
.then(() => {
console.log("✅ Update script finished")
process.exit(0)
})
.catch((error) => {
console.error("❌ Update failed:", error)
process.exit(1)
})
